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کاربرد سامانه گوگل ارث انجین و الگوریتم جنگلتصادفی در بررسی روند تغییرات کاربری اراضی(مطالعه موردی: بخشی از منطقه آبیک استان قزوین) | ||
| تحقیقات آب و خاک ایران | ||
| دوره 57، شماره 5، مرداد 1405، صفحه 1099-1117 اصل مقاله (1.66 M) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/ijswr.2026.412820.670120 | ||
| نویسندگان | ||
| آیه جاویدفر1؛ فریدون سرمدیان* 2 | ||
| 1گروه علوم و مهندسی خاک، دانشکده کشاورزی، دانشکدگان کشاورزی و منابع طبیعی، دانشگاه تهران، کرج، ایران. | ||
| 2عضو هیأت علمی گروه مهندسی علوم خاک، پردیس کشاورزی و منابع طبیعی دانشگاه تهران | ||
| چکیده | ||
| تحولات ناشی از تغییر کاربری و پوشش اراضی، بهویژه در اکوسیستمهای خشک و نیمهخشک، از جمله مهمترین مسائل اساسی در حوزه توسعه پایدار و حفاظت از محیطزیست به شمار میآید. این تحولات تأثیر مستقیمی بر پایداری منابع زیستمحیطی و امنیت غذایی دارد. پژوهش حاضر با هدف ترسیم سیر تغییر کاربری اراضی طی یک دوره چهلساله (۱۳۶۴ تا ۱۴۰۴) در محدودهای به مساحت تقریبی ۶۰ هزار هکتار در بخشی از شهرستان آبیک واقع در استان قزوین صورت گرفته است. بدین منظور، تصاویر ماهوارهای لندست در گوگل ارث انجین پردازش و با بهرهگیری از الگوریتم جنگل تصادفی طبقهبندی شدند. بهمنظور ارتقای دقت نتایج، علاوه بر باندهای طیفی، مجموعهای از متغیرهای مکمل شامل شاخصهای طیفی (NDVI, EVI, MNDWI, SAVI)، متغیرهای توپوگرافی و لایه مناطق انسانساخت جهانی (GHSL) در مدلسازی لحاظ گردید. کلاسهای کاربری مورد مطالعه در این پژوهش شامل اراضی مسکونی-صنعتی، کشاورزی آبی، کشاورزی دیم، باغات، مراتع، مراتع شور، مراتع بسیارشور و تالاب بودهاند. ارزیابی دقت نتایج، دقت کلی طبقهبندی را در بازه 91/0 تا 95/0 و ضریب کاپا را در محدوده 88/0 تا 93/0 نشان داد. نتایج پژوهش نشان میدهد که استفاده از دادههای ماهوارهای با قدرت تفکیک مکانی بالا و الگوریتم جنگل تصادفی میتواند به عنوان یک روش کارآمد برای تهیه نقشههای کاربری اراضی معرفی شود. یافتههای این مطالعه بر ضرورت تدوین برنامههای مدیریت کاربری اراضی با رویکرد مبتنی بر ظرفیت اکولوژیکی منطقه تأکید دارد، بهگونهای که توسعه منطقهای در مسیر حفاظت از منابع طبیعی و دستیابی به پایداری زیستمحیطی هدایت شود. | ||
| کلیدواژهها | ||
| سنجش از دور؛ شاخصهای طیفی؛ طبقهبندی کاربری اراضی؛ لندست؛ یادگیری ماشین | ||
| عنوان مقاله [English] | ||
| Application of Google Earth Engine Platform and Random Forest Algorithm in Investigating the Trend of Land Use Changes (Case Study: Part of the Abyek Region, Qazvin Province) | ||
| نویسندگان [English] | ||
| Ayeh Javidfar1؛ Fereydoon Sarmadian2 | ||
| 1Department of Soil Science, Faculty of Agriculture and Natural Resources, University of Tehran, Karaj, Iran. | ||
| 2soil science department< faculty of agricultural engineering and technology, university of Tehran | ||
| چکیده [English] | ||
| Transformations induced by land use and cover change, particularly in arid and semi-arid ecosystems, are among the most fundamental concerns in sustainable development and ecological conservation. Such alterations have direct implications for the resilience of natural resources and food security. This study aimed to trace land use changes over a 40-year period (1985–2025) in approximately 60,000 hectares of Abyek Region, Qazvin Province. For this purpose, Landsat satellite images were processed in the Google Earth Engine environment and classified using the Random Forest algorithm. In order to enhance the accuracy of the results, in addition to spectral bands, a set of complementary variables including spectral indices (NDVI, EVI, MNDWI, SAVI), topographic variables, and the Global Human Settlement Layer (GHSL) were incorporated into the modeling. The land use classes examined in this study included residential-industrial lands, irrigated agriculture, rainfed agriculture, orchards, rangelands, saline rangelands, highly saline rangelands, and wetlands. The accuracy assessment of the results indicated an overall accuracy ranging from 0.91 to 0.95 and a Kappa coefficient in the range of 0.88 to 0.93. The results of the study demonstrate that the use of high-spatial-resolution satellite data and the Random Forest algorithm can be introduced as an efficient method for preparing land use maps. The findings of this study emphasize the necessity of developing land use management programs with an approach based on the ecological capacity of the region, in such a way that regional development is directed toward the conservation of natural resources and the achievement of environmental sustainability. | ||
| کلیدواژهها [English] | ||
| Land Use Classification, Landsat, Machine Learning, Remote Sensing, Spectral Indices | ||
| مراجع | ||
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Aldiansyah, S., & Saputra, R. A. (2023). Comparison of machine learning algorithms for land use and land cover analysis using Google Earth engine (Case study: Wanggu watershed). International Journal of Remote Sensing and Earth Sciences (IJReSES), 19(2), 197-210. Amani, M., Ghorbanian, A., Ahmadi, S. A., Kakooei, M., Moghimi, A., Mirmazloumi, S. M., Moghaddam, S. H. A., Mahdavi, S., Ghahremanloo, M., Parsian, S., et al. (2020). Google Earth Engine cloud computing platform for remote sensing big data applications: A comprehensive review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5326–5350. Amani, M., Salehi, B., Mahdavi, S., & Brisco, B. (2018). Spectral analysis of wetlands using multi-source optical satellite imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 144, 19–36. Aminzadeh, Z., Esmail Ouri, A., Mostafazadeh, R., & Nasiri Khiavi, A. (2024). Assessing the performance of machine learning algorithms for analyzing land use change in the Hyrcanian forests of Iran. Environmental Science and Pollution Research, 31, 66056–66066 arekhi, S. , Ata, B. and shakooei, E. (2022). Evaluation of Vegetation/Land Use Change Techniques Using Satellite Images and GIS (Case Study: Gorganrood Basin). Physical Social Planning, 9(2), 41-60. doi: 10.30473/psp.2022.60210.2506. (In Persian). Atef, I., Ahmed, W., & Abdel-Maguid, R. H. (2023). Modelling of land use land cover changes using machine learning and GIS techniques: A case study in El-Fayoum Governorate, Egypt. Environmental Monitoring and Assessment, 195, 637 Atesoglu, A., Ozel, H. B., Varol, T., Cetin, M., Baysal, B. U., & Bulut, F. S. (2025). Monitoring land cover/use conversions in Türkiye wetlands using Collect Earth. Journal of the Indian Society of Remote Sensing, 53, 1979–1994. Atkinson, P. M., Jeganathan, C., Dash, J., & Atzberger, C. (2012). Inter-comparison of four models for smoothing satellite sensor time-series data to estimate vegetation phenology. Remote Sensing of Environment, 123, 400–417. Babazekri, F., Nooripour, M. and Karami Kalous, A. (2022). Economic evaluation of converting rice paddies into citrus orchards in the northern Rudpey section of Sari County. Agricultural Economics Research, 13(1), 25-44. (In Persian). Basukala, A. K., Oldenburg, C., Schellberg, J., Sultanov, M., & Dubovyk, O. (2017). Towards improved land use mapping of irrigated croplands: Performance assessment of different image classification algorithms and approaches. European Journal of Remote Sensing, 50(1), 187-201. Brown, C. F., Brumby, S. P., Guzder-Williams, B., Birch, T., Hyde, S. B., Mazzariello, J., Czerwinski, W., Pasquarella, V. J., Haertel, R., Ilyushchenko, S., et al. (2022). Dynamic World, near real-time global 10 m land use land cover mapping. Scientific Data, 9, 251. https://doi.org/10.1038/s41597-022-01307-5 Celleri, C., Zapperi, G., González Trilla, G., & Pratolongo, P. (2019). Assessing the capability of broadband indices derived from Landsat 8 Operational Land Imager to monitor above ground biomass and salinity in semiarid saline environments of the Bahía Blanca Estuary, Argentina. International Journal of Remote Sensing, 40(12), 4817-4838. Chen, D., Wang, Y., Shen, Z., Liao, J., Chen, J., & Sun, S. (2021). Long time-series mapping and change detection of coastal zone land use based on Google Earth Engine and multi-source data fusion. Remote Sensing, 14(1), 1. Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37(1), 35-46. Ebrahimi, S. A., Almodaresi, S. A., & Hamzeh, F. (2025). Modeling the discovery of changes and prediction of land use using optical sensors with land change modeler method (Study area: west of Tehran). Journal of Radar and Optical Remote Sensing and GIS, 8(3), 7–26. https://doi.org/10.71593/jrors.2025.1196762 Eskandari damaneh,H and Ghasemi Aryan,Y . (2025). Investigating the trend and explaining the key drivers of desertification and land degradation in Salehiyeh wetland and Qazvin salt plain. Integrated Watershed Management, 4(4), 81-93. doi: 10.22034/iwm.2024.2026209.1146. (In Persian). ghobadeyan, Z. , Alikhah Asl, M. and Rezvani, M. (2020). Investigating the Effects of Urban Development on Rangelands and Forests of Sirvan City Using Remote Sensing 1987-2016. Journal of Urban Ecology Researches, 11(21), 107-120. doi: 10.30473/grup.2020.7475. (In Persian). Ghorbanian, A., Kakooei, M., Amani, M., Mahdavi, S., Mohammadzadeh, A., & Hasanlou, M. (2020). Improved land cover map of Iran using Sentinel imagery within Google Earth Engine and a novel automatic workflow for land cover classification using migrated training samples. ISPRS Journal of Photogrammetry and Remote Sensing, 167, 276–288.https://doi.org/10.1016/j.isprsjprs.2020.07.013 Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment, 202, 18–27. Gumma, M. K., Thenkabail, P. S., Teluguntla, P. G., Oliphant, A., Xiong, J., Giri, C., ... & Whitbread, A. M. (2020). Agricultural cropland extent and areas of South Asia derived using Landsat satellite 30-m time-series big-data using random forest machine learning algorithms on the Google Earth Engine cloud. GIScience & Remote Sensing, 57(3), 302-322. Gurung, R. B., Breidt, F. J., Dutin, A., & Ogle, S. M. (2009). Predicting Enhanced Vegetation Index (EVI) curves for ecosystem modeling applications. Remote Sensing of Environment, 113(10), 2186–2193.https://doi.org/10.1016/j.rse.2009.05.015 Heydari,N . (2022). Review and analysis of policies and plans of enhancing wheat production and water productivity in Iran. Water Management in Agriculture, 9(1), 73-88. (In Persian). Holtgrave, A. K., Röder, N., Ackermann, A., Erasmi, S., & Kleinschmit, B. (2020). Comparing Sentinel-1 and -2 data and indices for agricultural land use monitoring. Remote Sensing, 12(18), 2919.https://doi.org/10.3390/rs12182919 Huang, S., Tang, L., Hupy, J. P., Wang, Y., & Shao, G. (2021). A commentary review on the use of normalized difference vegetation index (NDVI) in the era of popular remote sensing. Journal of Forestry Research, 32(1), 1–6. https://doi.org/10.1007/s11676-020-01155-1 Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309. Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83 (1-2), 195–213. Jabalbarezi, B. , Zehtabian, G. , Khosravi, H. , Barkhori, S. and Nosrati, K. (2023). Assessing land sensitivity to determine areas prone to wind erosion and dust production using the ILSWE Model. Desert, 28(2), 263-278. doi: 10.22059/jdesert.2023.97739 Kazemi Garajeh, M., Haji, F., Tohidfar, M., Sadeqi, A., Ahmadi, R., & Kariminejad, N. (2024). Spatiotemporal monitoring of climate change impacts on water resources using an integrated approach of remote sensing and Google Earth Engine. Scientific Reports, 14, 5469. Khan, Z., Saeed, A., & Bazai, M. H. (2020). Land use/land cover change detection and prediction using the CA-Markov model: A case study of Quetta city, Pakistan. Journal of Geography and Social Sciences, 2(2), 164-182. Li, X., Gong, P., Zhou, Y., Wang, J., Bai, Y., Chen, B., Hu, T., Xiao, Y., Xu, B., Yang, J., et al. (2020). Mapping global urban boundaries from the Global Artificial Impervious Area (GAIA) data. Environmental Research Letters, 15, 094044. Liu, C., Li, W., Zhu, G., Zhou, H., Yan, H., & Xue, P. (2020). Land use/land cover changes and their driving factors in the Northeastern Tibetan Plateau based on Geographical Detectors and Google Earth Engine: A case study in Gannan Prefecture. Remote Sensing, 12(19), 3139. Liu, Z. J., Ma, P. Y., Zhai, B. N., & Zhou, J. B. (2019). Soil moisture decline and residual nitrate accumulation after converting cropland to apple orchard in a semiarid region: Evidence from the Loess Plateau. CATENA, 181, 104080. Lotfi, P., & Ahmadi Nadoushan, M. (2024). Investigation of The Trend of Agricultural Land Use Changes in the Zayandeh Rood Watershed Using Google Earth Engine Platform. Environment and Interdisciplinary Development, 8(82), 35-48. Lukas, P., Melesse, A.M., & Kenea, T.T. (2023). Prediction of Future Land Use/Land Cover Changes Using a Coupled CA-ANN Model in the Upper Omo–Gibe River Basin, Ethiopia. Remote Sensing, 15(4), 1148. Madani, K. (2014). Water management in Iran: what is causing the looming crisis?. Journal of environmental studies and sciences, 4, 315-328. Madasa, A., Orimoloye, I. R., & Ololade, O. O. (2021). Application of geospatial indices for mapping land cover/use change detection in a mining area. Journal of African Earth Sciences, 175, 104108.https://doi.org/10.1016/j.jafrearsci.2021.104108 Manikandababu, C. S., Alzaben, N., Maashi, M., & Geetha, M. (2025). Mapping Coastal Urbanization Impacts with Object-Based Image Classification and Land use/Land Cover Change Detection: A Focus on Sustainable Development. Journal of South American Earth Sciences, 105559. Moghaddam,N and Kholghi,M . (2025). Analysis of Groundwater Table Decline and Salinity Intensification in the Qazvin Plain: Implications from a Water Resources Governance Perspective. Iranian Journal of Irrigation & Drainage, 19(3), 469-487. (In Persian). Mohammady, S., & Delavar, M. R. (2016). Urban sprawl assessment and modeling using landsat images and GIS. Modeling Earth Systems and Environment, 2, 1-14. Mohammadzade, S. , Sedighi, H. , Pezeshkir Rad, G. , Makhdom, M. and sharifi Kia, M. (2014). Analyzing the impacts of changing agronomic land use to orchard from the viewpoint of orchardist in the west of Urmia lake basin. Iranian Journal of Agricultural Economics and Development Research, 45(4), 775-785. doi: 10.22059/ijaedr.2014.53850. (In Persian). Mohiuddin, G., Mund, J.-P., & Rahaman, K. J. (2023). Detection of urban expansion using the indices-based built-up index derived from Landsat imagery in Google Earth Engine. GI_Forum, 1, 18–31. Molénat, J., Barkaoui, K., Benyoussef, S., Mekki, I., Zitouna, R., & Jacob, F. (2023). Diversification from field to landscape to adapt Mediterranean rainfed agriculture to water scarcity in climate change context. Current Opinion in Environmental Sustainability, 65, 101336. Moradi, Alireza, Jafari, Mohammad, Arzani, Hossein, Ebrahimi, Mahdieh. “Assessment of land use changes into dry land using satellite images and Geographical information system (GIS).” Journal of RS and GIS for Natural Resources, vol. 7, no. 1, 2016, pp. 89-100. (In Persian). Mousavi, S.R., Sarmadian, F., Omid, M., & Bogaert, P. (2022). Three-dimensional mapping of soil organic carbon using soil and environmental covariates in an arid and semi-arid region of Iran. Measurement, 201, 111706. Naboureh, A., Ebrahimy, H., Azadbakht, M., Bian, J., & Amani, M. (2020). RUESVMs: An ensemble method to handle the class imbalance problem in land cover mapping using Google Earth Engine. Remote Sensing, 12, 3484. Nasiri V, Deljouei A, Moradi F, Sadeghi SMM, Borz SA (2022) Land use and land cover mapping using Sentinel-2, Landsat-8 Satellite Images, and Google Earth Engine: A comparison of two composition methods. Remote Sensing, 14(9), 1977 Pérez-Cutillas, P., Pérez-Navarro, A., Conesa-García, C., Zema, D. A., & Amado-Álvarez, J. P. (2023). What is going on within Google Earth Engine? A systematic review and meta-analysis. Remote Sensing Applications: Society and Environment, 29, 100907. Pesaresi, M., Schiavina, M., Politis, P., Freire, S., Krasnodębska, K., Uhl, J. H., ... & Kemper, T. (2024). Advances on the Global Human Settlement Layer by joint assessment of Earth Observation and population survey data. International Journal of Digital Earth, 17(1), 2390454. Phan TN, Kuch V, Lehnert LW (2020) Land cover classification using Google Earth Engine and random forest classifier—the role of image composition. Remote Sens 12(15):2411 Rahmani, A. , Sarmadian, F. and Arefi, H. (2023). Digital modeling and prediction of soil subgroup classes using deep learning approach in a part of arid and semi-arid lands of Qazvin Plain. Iranian Journal of Soil and Water Research, 53(11), 2477-2499. doi: 10.22059/ijswr.2023.353339.669426. (In Persian). Shafizadeh-Moghadam, H., Minaei, F., Talebi-khiyavi, H., Xu, T., & Homaee, M. (2022). Synergetic use of multi-temporal Sentinel-1, Sentinel-2, NDVI, and topographic factors for estimating soil organic carbon. Catena, 212, 106077. Soil Survey Staff. (2022). Keys to Soil Taxonomy. 13th ed. USDA-Natural Resources Conservation Service, Washington DC. Stehman, S. V. (2009). Sampling designs for accuracy assessment of land cover. International Journal of Remote Sensing, 30 (20), 5243–5272. Tamiminia, H., Salehi, B., Mahdianpari, M., Quackenbush, L., Adeli, S., & Brisco, B. (2020). Google Earth Engine for geo-big data applications: A meta-analysis and systematic review. ISPRS Journal of Photogrammetry and Remote Sensing, 164, 152–170. Tesfaye, W., Elias, E., Warkineh, B., Tekalign, M., & Abebe, G. (2024). Modeling of land use and land cover changes using Google Earth Engine and machine learning approach: Implications for landscape anagement. Environmental Systems Research, 13, 31. Tsai, Y. H., Stow, D., An, L., Chen, H. L., Lewison, R., & Shi, L. (2019). Monitoring land-cover and land-use dynamics in Fanjingshan National Nature Reserve. Applied Geography, 111, 102077. Wang, S. W., Gebru, B. M., Lamchin, M., Kayastha, R. B., & Lee, W. K. (2020). Land use and land cover change detection and prediction in the Kathmandu district of Nepal using remote sensing and GIS. Sustainability, 12(9), 3925. Wu, H., Zhang, L., & Zhang, X. (2019). Cloud data and computing services allow regional environmental assessment: A case study of Macquarie-Castlereagh Basin, Australia. Chinese Geographical Science, 29(3), 394-404. Xu, H. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int. J. Remote Sens. 27, 3025–3033 (2006). Yan, X., & Wang, J. (2021). Dynamic monitoring of urban built-up object expansion trajectories in Karachi, Pakistan with time series images and the LandTrendr algorithm. Scientific Reports, 11, 23118. Zhao, Q., Yu, L., Li, X., Peng, D., Zhang, Y., & Gong, P. (2021). Progress and trends in the application of Google Earth and Google Earth Engine. Remote Sensing, 13, 3778. Zurqani, H. A., Post, C. J., Mikhailova, E. A., Schlautman, M. A., & Sharp, J. L. (2018). Geospatial analysis of land use change in the Savannah River Basin using Google Earth Engine. International journal of applied earth observation and geoinformation, 69, 175-185. | ||
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